Atiye Sabeghi
Papers
1
Total Citations
21
H-Index
1
About
Atiye Sabeghi has made significant contributions to the fields of optimization, neural networks, and robotics, with a particular focus on solving complex variational inequality problems. Her most cited work, "A new neural network framework for solving convex second-order cone constrained variational inequality problems with an application in multi-finger robot hands" (2019, 21 citations), introduces an innovative neural network model that leverages a smoothing method to transform variational inequality problems into convex second-order cone programming. This approach not only simplifies the solution process but also demonstrates practical utility in robotics, specifically in the control of multi-finger robot hands. Sabeghi’s research bridges theoretical optimization and real-world applications, offering high-performance computational tools for constrained systems. Her work is notable for its interdisciplinary impact, combining advanced mathematical modeling with engineering challenges. With a growing citation record, Sabeghi continues to influence researchers in optimization and robotics, providing efficient frameworks for tackling nonlinear and constrained problems. Her contributions are especially valuable for students and researchers seeking to apply neural networks to complex, real-world optimization tasks.
Research Focus
Key Achievements
Top Papers
- 1